Adapting classification rule induction to subgroup discovery

N. Lavrač, Peter A. Flach, Branko Kavšek, L. Todorovski
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引用次数: 41

Abstract

Rule learning is typically used for solving classification and prediction tasks. However learning of classification rules can be adapted also to subgroup discovery. This paper shows how this can be achieved by modifying the covering algorithm and the search heuristic, performing probabilistic classification of instances, and using an appropriate measure for evaluating the results of subgroup discovery. Experimental evaluation of the CN2-SD subgroup discovery algorithm on 17 UCI data sets demonstrates substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under the ROC curve.
将分类规则归纳法应用于子组发现
规则学习通常用于解决分类和预测任务。然而,分类规则的学习也可以适应于子组的发现。本文展示了如何通过修改覆盖算法和搜索启发式来实现这一点,执行实例的概率分类,并使用适当的度量来评估子组发现的结果。CN2-SD子群发现算法在17个UCI数据集上的实验评估表明,诱导规则数量大幅减少,规则覆盖率和规则显著性增加,ROC曲线下面积略有改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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